Analysis of commonly prescribed analgesics using in-silico processing of spectroscopic signals: application to surface water and industrial effluents, and comparative study via green and white assessments

Abstract

RESEARCH ARTICLEPrevious Contents Vol 19(7) Analysis of commonly prescribed analgesics using in-silico processing of spectroscopic signals: application to surface water and industrial effluents, and comparative study via green and white assessments Heba T. Elbalkiny A , Mohamed B. El-Zeiny B and Sarah S. Saleh https://orcid.org/0000-0002-1608-008X A * + Author Affiliations * Correspondence to: drsarahsalah@gmail.com

Handling Editor: Kurunthachalam Kannan

Environmental Chemistry 19(7) 446-459 https://doi.org/10.1071/EN22108 Submitted: 13 October 2022 Accepted: 6 December 2022 Published: 10 February 2023 © 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing.

Environmental context. Pharmaceuticals find their way to wastewater mainly through hospital and industrial effluents, and in turn affect all living organisms. The routine analysis of different water sources is tedious and of high cost. Our work presents a safe, low-cost method for analysing water samples to ensure proper cleanup of water and its suitability for human and animal use.

Rationale. Analgesics are one of the top classes of commonly prescribed drugs, and used over the counter. Therefore, they are most likely to be detected in wastewater samples coming from hospital and industrial effluents.

Methodology. This study focused on developing an in-silico UV spectroscopic manipulation of variant signal nature of low cost, using the methods of: advanced amplitude centring (AAC), mean centring of ratio spectra (MCR), successive derivative subtraction (SDS) and continuous wavelet transformation (CWT), for the determination of a ternary mixture of three analgesics: paracetamol (PCM), diclofenac (DCF) and ibuprofen (IBU) in water samples after sample cleanup using dispersive liquid–liquid microextraction (DLLME).

Results. The proposed methods were compared to those reported in terms of greenness, simplicity and effectiveness using the greenness assessment tools (Eco-scale & AGREE) and white analytical chemistry (WAC) tool. The AAC method showed the highest scores: an Eco-scale of 71, AGREE of 0.55 and RGB of 84.4 when compared to the reported methods.

Discussion. The AAC method was applied effectively for the study of surface water samples and industrial effluents with high accuracy and precision. Thus, real water samples could be routinely analysed with minimal cost to ensure proper cleanup of water and its suitability for human and animal use.